Bluetooth based smart sensor network Essay

Currently, huge electronic info repositories ar being maintained by banks and otherwise fiscal institutions. worth(predicate) bits of teaching atomic number 18 embed in these teaching repositories. The huge size of it of these info sources rent it im achievable for a man analyst to uprise up with inte liveing selective information (or patterns) that get out protagonist in the finis do abut. A number of commercial enterprises bewilder been quick to write out the tax of this concept, as a consequence of which the softw atomic number 18 merchandise itself for selective information archeological site is judge to be in excess of 10 1 thousand thousand USD. This paper is intended for those who would like to get aw be of the possible finishs of data exploit to enhance the performance of some of their core line of descent processes. In this paper discussion is about the broad aras of application, like attempt management, portfolio management, work, gu est indite and node armorial bearing, where data mine techniques evict be utilise in banks and other pecuniary institutions to enhance their air performance. INTRODUCTIONAs noesis is becoming more and more synonymous to wealth induction and as a strategy plan for competing in the grocery enthrone digest be no bump than the information on which it is base, the grandness of knowledge and information in todays business mess never be seen as an exogenous factor to the business. Organizations and singulars having approach to the decent information at the right moment, defend greater chances of being lucky in the epoch of globalization and cut-throat competition. Business Intelligence focuses on discovering knowledge from unhomogeneous electronic data repositories, both internal and external, to backup man better termination fashioning.  entropy mining techniques become important for this knowledge discovery from databases. In recent years, business intellig ence activity systems have played diametrical roles in helping organizations to fine tune the business goals such as improving guest retention, commercialize penetration, profitability and efficiency. In most roles, these insights are driven by analyses of diachronical data. Global competitions, dynamic markets, and rapidly decrease cycles of technological innovation give important challenges for the banking and finance exertion. Worldwide just-in- snip availableness of information allows enterprises to purify their flexibility. In fiscal institutions considerable developments in information engine room have led to huge demand for continuous depth psychology of resulting data.selective information mining piece of tail contribute to solving business problems in banking and finance by conclusion patterns, causalities, and correlations in business information and market prices that are non immediately apparent to managers because the volume data is in addition large or is generated too quickly to screen by experts. The managers of the banks whitethorn go a step further to find the sequences, episodes and periodicity of the deed deportment of their guests which may help them in actually better constituenting, targeting, acquiring, retaining and maintaining a profitable node base. Business Intelligence and data mining techniques cease as well as help them in poseing various classes of clients and come up with a class base product and/or pricing approach that may garner better r scourue management as well. The broad categories of application of data archeological site and Business Intelligence techniques in the banking and fiscal industry vertical may be viewed as follows bump worryManaging and metre of hazard is at the core of every financial institution. right aways major(ip) challenge in the banking and insurance world is therefore the executing of jeopardy management systems in order to identify, posting, and control busine ss exposure. here(predicate) character reference and market happen present the central challenge, one back observe a major change in the area of how to measure and deal with them, base on the advent of advanced database and data mining technology.( Other types of put on the line is also available in the banking and finance i.e., liquid state lay on the line, operational risk, or concentration risk. ) Today, integrated measurement of different kinds of risk (i.e., market and credit risk) is moving into focus. These all are based on rides representing single financial actors or risk factors, their doings, and their interaction with overall market, making this field loftyly important topic of research. Financial Market RiskFor single financial instruments, that is, stock indices, interest rates, or currencies, market risk measurement is based on bewilders depending on a come out of profound risk factor, such as interest rates, stock indices, or scotch development. O ne is interested in a functional form amid instrument price or risk and underlying risk factors as well as in functional dependency of the risk factors itself. Today different market risk measurement approaches exist. All of them rely on models representing single instrument, their behaviour and interaction with overall market. Many of this open fire but be built by using various data mining techniques on the proprietary portfolio data, since data is not publicly available and of necessity consistent supervision. Credit RiskCredit risk assessment is anchor component in the process of commercial lending. Without it the lender would be futile to make an objective judgement of weather to lend to the prospective borrower, or if how much charge for the loan. Credit risk management washstand be classified into two basic groupsCredit scoring/credit pass judgment Assignment of a client or a product to risk level. (i.e., credit approval) Behaviour scoring/credit rank migration compe ndium. Valuation of a customers or products probability of a change in risk level within a devoted snip. (i.e., default rate volatility) In commercial lending, risk assessment is ordinarily an attempt to assess the risk of passing to the lender when making a limited lending decision. Here credit risk eject quantify by the changes of value of a credit product or of a whole credit customer portfolio, which is based on change in the instruments ranting, the default probability, and recovery rate of the instrument in case of default. Further diversification effects influence the result on a portfolio level. Thus a major part of implementation and care ofcredit risk management system will be a typical data mining problem the modelling of the credit instruments value by means of the default probabilities, rating migrations, and recovery rates. triple major approaches exist to model credit risk on the effect level accounting analytic approaches, statistical prediction and option metaphysical approaches. Since large amount of information about client exist in financial business, an adequate way to build such models is to use their declare database and data mining techniques, fitting models to the business needs and the business up-to-the-minute credit portfolio.Portfolio ManagementRisk measurement approaches on an aggregated portfolio level quantify the risk of a set of instrument or customer including diversification effects. On the other hand, forecasting models give an induction of the expected re change shape or price of a financial instrument. Both make it possible to manage firm wide portfolio actively in a risk/return efficient manner. The application of modern risk possibleness is therefore within portfolio theory, an important part of portfolio management. With the data mining and optimization techniques investors are able to allocate capital crossways trading activities to maximize profit or minimise risk. This feature supports the ability to g enerate flock recommendations and portfolio structuring from user supplied profit and risk requirement. With data mining techniques it is possible to provide extensive scenario analysis capabilities concerning expected asset prices or returns and the risk involved. With this functionality, what if simulations of change market conditions e.g. interest rate and exchange rate changes) cab be run to assess impact on the value and/or risk associated with portfolio, business unit counterparty, or trading desk. Various scenario results outhouse be regarded by considering actual market conditions. Profit and loss analyses allow users to access an asset class, region, counterparty, or custom sub portfolio so-and-so be benchmarked against common international benchmarks. TradingFor the last few years a major topic of research has been the building of quantitative trading tools using data mining methods based on by data as  enter to predict short-term movements of important currencie s, interest rates, or equities. The goal of this technique is to spot prison terms when markets are cheap or expensive by identifying the factor that are important in determining market returns. The trading system examines the relationship between relevant information and piece of financial assets, and gives you buy or sell recommendations when they suspect an under or overvaluation. Thus, even if some traders find the data mining approach too machinelike or too risky to be used systematically, they may exigency to use it selectively as further opinion. Trading is based on the idea of predicting short term movements in the price/value of a product (currency/equity/interest rate etc.). With a reasonable gauge in place one may trade the product if he/she thinks it is going to be over wanted or undervalued in the coming in store(predicate). Trading traditionally is done based on the instinct of the trader. If he/she thinks the product is not priced properly he/she may sell/buy it. This instinct is unremarkably based on past experience and some analysis based on market conditions.However, the number of factors that even the most expert of traders locoweed account for are limited. Hence, quite often these predictions fail. The price of a financial asset is influenced by a descriptor of factors which can be more often than not classified as economic, political and market factors. Participants in a market observe the relation between these factors and the price of an asset, account for the current value of these factors and predict the futurity set to finally arrive at the future value of the asset and trade accordingly. Quite often by the time a trained eye detects these favourable factors, many others may have discovered the opportunity, decreasing the possible revenues differently. Also these factors in turn may be related to several other factors making prediction difficult. Data mining techniques are used to discover unfathomable knowledge, unknown patterns and reinvigorated overlooks from large data sets, which may be useful for a variety of decision making activity.With the increasing economic globalization and emendments in information technology, large amounts of financial data are being generated and stored. subjected to data mining techniques to discover hidden patterns and obtain predictions for trends in the future and the behaviour of the financial markets. With the immediacy offered by data mining, latest data can be mined to obtain crucial information at the earliest. This in turn would result in an improved market place responsiveness and sentiency leading to reduced costs and increased revenue. Advancements made in technology have enabled to create faster and better prediction systems. These systems are based on a combination of data mining techniques and artificial intelligence methods like Case Based Reasoning ( cosmic microwave background radiation) and Neural Networks (NN). A combination of such a for ecasting system together with a peachy trading strategy offers tremendous opportunities for massive returns. The value of a financial asset is dependent on both macroeconomic and microeconomic variables and this data is available in a variety of disparate formats. NN and CBR techniques can be applied extensively for predicting these financial variables. NN are characterized by learnedness capabilities and the ability to improve performance over time. Also NN can derive i.e. recognize modernistic objects which may be similar but not exactly identical to previous objects.NN with their ability to derive meaning from inaccurate data can be used to detect patterns which are otherwise too complex to be detected by valets. NN act as experts in the area that they have been trained to work in. these can be used to provide predictions for new situations and work in real time. Thus, historic data available about financial markets and the various variables can be used to train NN to simul ate the market. CBR methodology is based on reasoning from past performances. It uses a large repository of data stored as cases which would include all the market variables in this case. When a new case is fed in (in the form of a case containing the concerned variables), the CBR algorithm predicts the performance/result of this case based on the cases it has in its repository. Data mining techniques can be used to detect hidden patterns in these cases which may then be used for further decision making. CBR methods can be used in real time which makes analysis really quick and helps in real time decision making resulting in immediate profits. Thus data mining and business intelligence (CBR and NN) techniques may be used in conjunction in financial markets to predict market behaviour and obtain patterned behaviour to influence decision making. Customer profile and Customer Relationship ManagementBanks have many and huge databases containing transactional and other details of its cus tomers. Valuable business information can be extracted from these data stores. But it is unfeasible to support analysis and decision makingusing traditional query languages because human analysis breaks down with volume and dimensionality. Traditional statistical methods do not have the capacity and scale to analyse these data, and hence modern data mining methodologies and tools are increasingly being used for decision making process not plainly in banking and financial institutions, but across the industries. Customer profiling is a data mining process that builds customer profiles of different groups from the companys existing customer database. The information obtained from this process can be used for different purposes, such as mind business performance, making new merchandising initiatives, market segmentation, risk analysis and revising company customer policies. The advantage of data mining is that it can handle large amounts of data and learn inherent structures and patt erns in data. It can generate rules and models that are useful in enabling decisions that can be applied to future cases. Customer Behaviour Modeling (CBM) or customer profiling is a tool to predict the future value of an individual and the risk category to which he belongs to based on his demographic characteristics, lifestyle and previous behaviour. This helps to focus on customer retention. The two important facts that have important implication in selecting customer profiling methods are Profiling information can consist of many variables (or dozens of them). Majority of them are categorical variables (or non-numeric variables or nominal variables).Customer profiling is to characterize features of particular(prenominal) customer groups. Many data mining techniques search profiles of special customer groups systematically using Artificial Intelligence techniques. They generate accurate profiles based on beam search and incremental learning techniques. Customer profiling also uses many predictive modeling methods. Predictive modelling techniques applicable can be categorized into two broad approaches. They depend on the type of predicted information or variables, also called target variables. If the type of predicted set is categorical, classification techniques is preferred to be used. Classification MethodsIn this approach, risk levels are organized into two categories based on past default history. For example, customers with past default history can be classified into risky group, whereas the rest are placed as safe group. development this categorization information as target of prediction, Decision Tree and Rule Induction techniques can be used to build models that can predict default risk levels of new loan applications. Value Prediction MethodsIn this method, for example, instead of classifying new loan applications, it attempts to predict expected default amounts for new loan applications. The predicted values are numeric and thus it requir es modelling techniques that can wad numeric data as target (or predicted) variables. Neural Network and regression are used for this purpose. The most common data mining methods used for customer profiling are Clustering (descriptive) Classification (predictive) and regression (predictive) Association rule discovery (descriptive) and sequential pattern discovery (predictive)In CRM, data mining is frequently used to assign a score to a particular customer or prospect indicating the likelihood that the individual will practise in a particular way. For example, a score could measure the aptness to respond to a particular insurance or credit identity card offer or to switch to a competitors product. Data mining can be useful in all the ternion phases of a customer relationship-cycle customer acquisition, increasing value of the customer and customer retention. For example, a typical banking firm let say sends 1 million direct mails for credit card customer acquisition. Past rese arches have shown that typically 6% of such target customers respond to these direct mails. Banks use their credit risk models to classify these respondents in good credit risk and rotten credit risk classes. The proportion of good credit risk respondents is barely 16% out of the total respondents. So, as net result, roughly only 1% of the total targeted customers are converted into the credit card customers through direct mailing. Seeing the huge cost and effort involved in such marketing process, data mining techniques can significantly improve the customer conversion rate by more focused marketing. Using a predictive test model using decision channelise techniques like CHAID (Chi-squared Automatic Interaction Detection),CART (Classification And Regression Trees), Quest and C5.0 it can beanalyzed which customers are more probable to respond. And using this with the risk model using techniques like neural network can help build a test model. The way data mining can actually be b uilt into the CRM application is determined by the temperament of customer interaction. The customer interaction could be inbound (when the customer contacts the firm) or outbound (when the firm contacts customers). The deployment requirements are quite different. Outbound interactions such as direct Building Profitable Customer Relations with Data Mining, Herb Edelstein mail campaign involve the firm selecting the people whom to be mailed by applying the test model to the customer database. In other outbound campaigns like advertising, the profile of good prospects shown by the test model needs to be matched to the profile of the people the advertisement would reach. For inbound legal proceeding such as telephone or internet order, the application moldiness respond in real time. Therefore the data mining model is embedded in the application and actively recommends an action. In either case, one of the key issues in applying a model to new data set is the transformations that are made in building the model. The ease with which these changes are embedded in the model determines the productivity of deploying these tools. Marketing and customer careBecause high competitions in the finance industry, intelligent business decisions in marketing are more important than ever for better customer targeting, acquisition, retention and customer relationship. There is a need for customer care and marketing strategies to be in place for the success and survival of the business. It is possible with the help of data mining and predictive analytics to make such strategies. Financial institutions are finding it more difficult to locate new previously unsolicited buyers, and as a result they are implementing aggressive marketing program to vex new customer from their competitors. The uncertainties of the buyer make planning of new operate and media usage almost impossible. The classical solution is to apply subjective human expert knowledge as rules of thumb. Until recently, replacing the human expert by computer technology has been difficult.An interesting tool available in marketing and financial institution is analysis of clients data. This allows analysis and figuring of key indicators that help bank to identify factors that affected customers demand in the past and customer need in the future. randomness about the customers personal data can also give indications that affect future demand. In case of analysis of sell debtors and small corporations, marketing tasks will typically include factors about the customer himself, his credit record and rating made by external rating agencies. With the advent of data mining and business intelligence tools it has become possible for banks to strengthen their customer acquisition by direct marketing and assemble multi- channel contacts, to improve customer development by cross merchandising and up selling of products, and to increase customer retention by behaviour management.It is possible for the banks to use the data available to retain its best customers and to identify opportunities to sell them additional services. The profiling of all the valuable accounts can be done and the top most say 5-10 % can be appoint to Relationship Managers, whose job will be to identify new selling opportunities with these customers. It is also possible to bundle various offers to meet the need of the valued customers. Data mining can also help the banks in customizing the various promotional offers. For example the direct mails can be customized as per the segment of the account holders in the bank. It is also possible for the banks to find out thepr oblem customers who can be defaulters in the future, from their past payment records and the profile and the data patterns that are available. This can also help the banks in adjusting the relationship with these customers so that the loss in future is kept to its minimum.Data mining can improve the response rates in the direct mail campaigns as th e time required to classify the customers will be reduced, this in turn will increase the revenues, improve the sales force efficiency from the target group. Data mining helps the banks to optimize their portfolio of services, delivery channels. A record of past transactions can give useful insight to the bank and different locations /branches of like branch can also follow some patterns that when noticed can be used as past records to learn from and base the future actions upon.Data Mining techniques can be of immense help to the banks and financialinstitutions in this arena for better targeting and acquiring new customers, fraud undercover work in real time, providing segment based products for better targeting the customers, analysis of the customers secure patterns over time for better retention and relationship, detection of emerging trends to take proactive stance in a highly competitive market adding a lot more value to existing products and services and launch of new pr oduct and service bundles. Reference